arXiv subjects

Debranjan Pal

Publications and source records attributed to Debranjan Pal.

3 recordsLinked to original sources

arXiv2024

Similarity analysis of DNA sequences through local distribution of nucleotides in strategic neighborhood

We propose a new alignment-free algorithm by constructing a compact vector representation on $\mathbb{R}^{24}$ of a DNA sequence of arbitrary length. Each component of this vector is obtained from a representative sequence, the elements of which are the values realized by a function $Γ$. This function $Γ$ acts on neighborhoods of arbitrary radius that are located at strategic positions within the DNA sequence and carries complete information about the local distribution of frequencies of the nucleotides as a consequence of the uniqueness of prime factorization of integer. The algorithm exhibits linear time complexity and turns out to consume significantly small memory. The two natural parameters characterizing the radius and location of the neighbourhoods are fixed by comparing the phylogenetic tree with the benchmark for full genome sequences of fish mtDNA datasets. Using these fitting parameters, the method is applied to analyze a number of genome sequences from benchmark and other standard datasets. Our algorithm proves to be computationally efficient compared to other well known algorithms when applied on simulated dataset.

cs.DS↗

arXiv2024

Modeling Linear and Non-linear Layers: An MILP Approach Towards Finding Differential and Impossible Differential Propagations

Symmetric key cryptography stands as a fundamental cornerstone in ensuring security within contemporary electronic communication frameworks. The cryptanalysis of classical symmetric key ciphers involves traditional methods and techniques aimed at breaking or analyzing these cryptographic systems. In the evaluation of new ciphers, the resistance against linear and differential cryptanalysis is commonly a key design criterion. The wide trail design technique for block ciphers facilitates the demonstration of security against linear and differential cryptanalysis. Assessing the scheme's security against differential attacks often involves determining the minimum number of active SBoxes for all rounds of a cipher. The propagation characteristics of a cryptographic component, such as an SBox, can be expressed using Boolean functions. Mixed Integer Linear Programming (MILP) proves to be a valuable technique for solving Boolean functions. We formulate a set of inequalities to model a Boolean function, which is subsequently solved by an MILP solver. To efficiently model a Boolean function and select a minimal set of inequalities, two key challenges must be addressed. We propose algorithms to address the second challenge, aiming to find more optimized linear and non-linear components. Our approaches are applied to modeling SBoxes (up to six bits) and EXOR operations with any number of inputs. Additionally, we introduce an MILP-based automatic tool for exploring differential and impossible differential propagations within a cipher. The tool is successfully applied to five lightweight block ciphers: Lilliput, GIFT64, SKINNY64, Klein, and MIBS.

cs.CR↗

arXiv2023

Efficient Algorithms for Modeling SBoxes Using MILP

Mixed Integer Linear Programming (MILP) is a well-known approach for the cryptanalysis of a symmetric cipher. A number of MILP-based security analyses have been reported for non-linear (SBoxes) and linear layers. Researchers proposed word- and bit-wise SBox modeling techniques using a set of inequalities which helps in searching differential trails for a cipher. In this paper, we propose two new techniques to reduce the number of inequalities to represent the valid differential transitions for SBoxes. Our first technique chooses the best greedy solution with a random tiebreaker and achieves improved results for the 4-bit SBoxes of MIBS, LBlock, and Serpent over the existing results of Sun et al. [25]. Subset addition, our second approach, is an improvement over the algorithm proposed by Boura and Coggia. Subset addition technique is faster than Boura and Coggia [10] and also improves the count of inequalities. Our algorithm emulates the existing results for the 4-bit SBoxes of Minalpher, LBlock, Serpent, Prince, and Rectangle. The subset addition method also works for 5-bit and 6-bit SBoxes. We improve the boundary of minimum number inequalities from the existing results for 5-bit SBoxes of ASCON and SC2000. Application of subset addition technique for 6-bit SBoxes of APN, FIDES, and SC2000 enhances the existing results. By applying multithreading, we reduced the execution time needed to find the minimum inequality set over the existing techniques.

cs.CR↗